Fuzzy model reference control with adaptation mechanism

•We included input fuzzy sets in the adaptation mechanism.•We modified the adaptation mechanism of FMRLC.•Simulations proved an improvement of control performance.

An improved approach to adaptation in fuzzy model reference learning control (FMRLC) will be introduced in this paper. The main idea of the presented method consists in including into adaptation process the input membership functions in the fuzzy controller. In comparison with original FMRLC algorithm the proposed method can be started with smaller number of input membership functions and reduces amount of penalization after few steps that results in convergent rule base and better and more reliable behavior of the closed loop that is shown on an simulation example of control of a nonlinear time-varying system.